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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by letting the model issue constrained symbolic commands that execute server-side against in-memory handles, so only compact result stubs—rather than raw text—travel through the conversation. It also provides server-side memo storage with expandable handles, letting agents retain and retrieve large context across long sessions at a fraction of the token cost.
      4 npm
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by having the model emit constrained symbolic S-expression commands that are validated and executed server-side, with large result sets and stashed context kept behind compact handle stubs rather than roundtripped as full text. Supports recursive sub-queries and memo storage so agents can search, filter, aggregate, and recall document content token-efficiently.
      4 npm
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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by executing symbolically-constrained Nucleus commands server-side instead of arbitrary generated code, without vector databases or chunking. It also lets agents stash large text results as compact in-memory handles and memos, cutting token usage by 90%+ while keeping full data retrievable on demand.
      4 npm
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by executing constrained symbolic Nucleus commands against them server-side, with in-memory handle storage that returns compact stubs instead of full result sets. Also provides a server-side memory pad so agents can stash context and retrieve it later, cutting token usage dramatically.
      4 npm
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables token-efficient analysis of very large documents by letting an LLM issue constrained symbolic commands while results and context are stored server-side as compact handles and memos. This avoids loading full documents or large intermediate outputs into the model context.
      4 npm
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables an LLM to analyze documents far larger than its context window by issuing symbolic Nucleus commands that are parsed, type-checked, and executed server-side against the document. Results are kept in in-memory handles and a persistent memory pad so agents carry compact stubs instead of large text blobs, cutting token usage dramatically.
      4 npm
      MIT

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